Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "164" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 39 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 37 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459854 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.931064 | -1.027139 | -0.847046 | 0.467648 | -1.007763 | 1.014166 | 0.097277 | 5.960865 | 0.7292 | 0.7486 | 0.4317 | 3.401150 | 2.832879 |
| 2459853 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.757432 | -0.730886 | -0.952982 | 0.763406 | -0.917130 | 0.631378 | 0.600373 | 7.313578 | 0.7516 | 0.7022 | 0.4171 | 3.699188 | 3.291651 |
| 2459852 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 8.65% | 1.62% | -1.061475 | -0.563492 | -0.877824 | 1.109698 | -0.981450 | 0.409352 | -0.652271 | 0.786929 | 0.8380 | 0.8373 | 0.2349 | 3.420602 | 2.980500 |
| 2459851 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.931895 | -0.508530 | -0.827063 | 1.120987 | 0.709837 | 0.705793 | 1.451688 | 4.299540 | 0.7703 | 0.7466 | 0.3314 | 3.897546 | 3.227928 |
| 2459850 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.139924 | -1.071220 | -0.879687 | 0.763694 | -0.733884 | 1.190358 | 0.970430 | 6.737230 | 0.7566 | 0.7646 | 0.3441 | 3.612928 | 2.920413 |
| 2459849 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 16.67% | 0.00% | -1.049307 | -1.267599 | -0.482184 | 0.845358 | -1.072931 | 0.914635 | 0.499699 | 3.840039 | 0.7548 | 0.7577 | 0.3486 | 1.670690 | 1.523556 |
| 2459848 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 30.15% | 0.00% | -0.558899 | -1.029893 | 0.157236 | -0.019422 | -1.052052 | 1.096566 | 0.090247 | 2.008883 | 0.7315 | 0.7593 | 0.3702 | 1.677120 | 1.511079 |
| 2459847 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 3.21% | 0.00% | -0.068314 | -0.767898 | 0.206428 | 0.383086 | -0.866392 | 1.095546 | 0.578898 | 2.895887 | 0.7320 | 0.6947 | 0.4243 | 1.767666 | 1.605198 |
| 2459846 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 33.33% | 0.00% | -0.684111 | -1.101526 | 0.837286 | -0.002578 | -1.272141 | 0.786389 | 0.001935 | 1.803352 | 0.8359 | 0.6754 | 0.4904 | 1.857479 | 1.484431 |
| 2459845 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 16.02% | 0.00% | -0.447771 | -0.794395 | 0.465701 | -0.735519 | -0.679872 | 1.055400 | -0.217233 | 1.619202 | 0.7325 | 0.7472 | 0.3788 | 1.248510 | 1.196752 |
| 2459844 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.911341 | -0.229740 | -1.021811 | 0.458432 | -0.301855 | 2.997633 | 0.352529 | 2.987513 | 0.0901 | 0.1212 | 0.0203 | nan | nan |
| 2459843 | digital_ok | 100.00% | 1.20% | 0.66% | 0.00% | 100.00% | 0.00% | -0.599452 | -1.165370 | -0.653538 | 4.468012 | -1.315144 | 5.178516 | -0.419662 | 1.933986 | 0.7472 | 0.7397 | 0.3959 | 4.954533 | 3.685399 |
| 2459842 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.500250 | -0.848958 | -0.192888 | 2.849751 | 0.284537 | 0.714831 | -0.106069 | 0.625824 | 0.7649 | 0.6856 | 0.2479 | 2.322948 | 2.165346 |
| 2459841 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 2.353645 | 5.587396 | -0.607355 | 2.789858 | 3.550667 | 22.305110 | 2.173711 | 3.018231 | 0.0903 | 0.1049 | 0.0285 | nan | nan |
| 2459840 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 134.737310 | 190.919882 | 62.943153 | 89.521803 | 439.824918 | 1050.477833 | 1118.496177 | 2231.503197 | 0.0176 | 0.0162 | 0.0010 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.61% | -0.689841 | -0.920870 | -0.988082 | -0.463877 | -1.442236 | -0.252215 | 0.151278 | 1.020439 | 0.7588 | 0.7280 | 0.3903 | 2.302992 | 2.203725 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0387 | 0.0678 | 0.0081 | nan | nan |
| 2459835 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | -0.392521 | -1.096534 | -1.083761 | 0.302748 | -0.554057 | 0.861682 | 0.219525 | 0.073985 | 0.0387 | 0.0673 | 0.0070 | nan | nan |
| 2459833 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 0.037827 | -0.058319 | -0.518248 | 0.179399 | 1.879935 | 2.068204 | 1.449844 | 1.257953 | 0.0310 | 0.0361 | 0.0053 | nan | nan |
| 2459832 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.965279 | -1.508570 | -1.159467 | -0.504167 | -1.083442 | -0.162307 | 0.393753 | 1.598992 | 0.8136 | 0.5570 | 0.5765 | 1.856859 | 1.817886 |
| 2459831 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 1.451097 | 1.959743 | -0.594198 | 0.542825 | 2.180570 | 2.529960 | 1.262668 | 1.142364 | 0.0294 | 0.0284 | 0.0013 | nan | nan |
| 2459830 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 2.63% | -1.775951 | -1.570831 | -1.145823 | -0.496914 | -0.633560 | 0.374031 | 0.668831 | 1.954648 | 0.8117 | 0.5629 | 0.5688 | 1.667215 | 1.617125 |
| 2459829 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.591805 | -1.744314 | -1.013155 | -0.613822 | -0.998354 | -0.444845 | 1.132919 | 3.860297 | 0.7612 | 0.6883 | 0.4113 | 1.266030 | 1.045948 |
| 2459828 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 55.26% | 44.74% | -0.955536 | -1.353628 | -0.936985 | -0.580691 | -0.724465 | 0.393878 | 0.456196 | 1.254847 | 0.8100 | 0.5736 | 0.5488 | 0.000000 | 0.000000 |
| 2459827 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.530206 | -0.629968 | -0.736417 | 0.425404 | -0.499359 | 19.994449 | 0.719159 | 2.416603 | 0.7723 | 0.6975 | 0.4062 | 0.000000 | 0.000000 |
| 2459826 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 28.95% | 55.26% | -1.165266 | -1.311671 | -0.845106 | -0.732171 | -1.133669 | -0.284489 | 3.102420 | 1.610292 | 0.8117 | 0.6080 | 0.5058 | inf | inf |
| 2459825 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.058802 | -0.981195 | -0.861187 | -0.340072 | 6.735090 | 6.745734 | 5.618963 | 4.653924 | 0.8090 | 0.6152 | 0.5087 | 5.339574 | 5.879677 |
| 2459824 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -1.737120 | -1.204536 | -0.753270 | -0.289831 | 5.374617 | 5.647878 | 19.343149 | 10.423201 | 0.7420 | 0.7573 | 0.3519 | 4.589130 | 6.203419 |
| 2459823 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.751086 | -1.413271 | -0.547635 | -0.630496 | -0.415645 | -0.690112 | -0.071578 | 0.720728 | 0.7772 | 0.6697 | 0.4531 | 2.913442 | 2.958160 |
| 2459822 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.990601 | -1.283345 | -0.725418 | -0.753682 | 0.684958 | 0.749105 | 1.438153 | 0.788568 | 0.8179 | 0.6464 | 0.4980 | 2.080178 | 1.912494 |
| 2459821 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.149270 | -0.615495 | -0.798047 | -1.003947 | -0.006517 | 0.340616 | 0.454017 | 0.363421 | 0.8019 | 0.6413 | 0.5060 | 2.038266 | 2.066235 |
| 2459820 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 1.60% | -0.556028 | -1.397079 | -0.849979 | -0.878833 | -1.255444 | -0.324803 | 0.297748 | 1.643809 | 0.7838 | 0.6979 | 0.4089 | 2.358850 | 2.032974 |
| 2459817 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -0.495705 | -0.965821 | -0.995395 | -0.893566 | -0.771967 | -0.752331 | 0.894536 | 0.787647 | 0.8097 | 0.6716 | 0.4987 | 2.239973 | 2.239462 |
| 2459816 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.157818 | -1.188442 | -1.290107 | -0.092556 | -1.204611 | -0.460462 | 0.497902 | 0.342429 | 0.8486 | 0.6145 | 0.5782 | 2.129774 | 1.819360 |
| 2459815 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.296362 | -1.268657 | -1.209108 | -0.442444 | -1.350061 | -0.359713 | -0.093713 | 1.259115 | 0.7985 | 0.6747 | 0.5136 | 2.286386 | 2.171537 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | -1.076654 | -1.841286 | -1.019640 | -1.082735 | -1.885984 | -1.072860 | 0.075846 | 2.327424 | 0.7979 | 0.7089 | 0.4059 | 2.821982 | 2.156941 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 5.960865 | -1.027139 | -0.931064 | 0.467648 | -0.847046 | 1.014166 | -1.007763 | 5.960865 | 0.097277 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 7.313578 | -0.730886 | -0.757432 | 0.763406 | -0.952982 | 0.631378 | -0.917130 | 7.313578 | 0.600373 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Power | 1.109698 | -1.061475 | -0.563492 | -0.877824 | 1.109698 | -0.981450 | 0.409352 | -0.652271 | 0.786929 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 4.299540 | -0.931895 | -0.508530 | -0.827063 | 1.120987 | 0.709837 | 0.705793 | 1.451688 | 4.299540 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 6.737230 | -1.139924 | -1.071220 | -0.879687 | 0.763694 | -0.733884 | 1.190358 | 0.970430 | 6.737230 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 3.840039 | -1.049307 | -1.267599 | -0.482184 | 0.845358 | -1.072931 | 0.914635 | 0.499699 | 3.840039 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 2.008883 | -1.029893 | -0.558899 | -0.019422 | 0.157236 | 1.096566 | -1.052052 | 2.008883 | 0.090247 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 2.895887 | -0.767898 | -0.068314 | 0.383086 | 0.206428 | 1.095546 | -0.866392 | 2.895887 | 0.578898 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 1.803352 | -0.684111 | -1.101526 | 0.837286 | -0.002578 | -1.272141 | 0.786389 | 0.001935 | 1.803352 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 1.619202 | -0.794395 | -0.447771 | -0.735519 | 0.465701 | 1.055400 | -0.679872 | 1.619202 | -0.217233 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Variability | 2.997633 | -0.911341 | -0.229740 | -1.021811 | 0.458432 | -0.301855 | 2.997633 | 0.352529 | 2.987513 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Variability | 5.178516 | -1.165370 | -0.599452 | 4.468012 | -0.653538 | 5.178516 | -1.315144 | 1.933986 | -0.419662 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Power | 2.849751 | -0.500250 | -0.848958 | -0.192888 | 2.849751 | 0.284537 | 0.714831 | -0.106069 | 0.625824 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Variability | 22.305110 | 2.353645 | 5.587396 | -0.607355 | 2.789858 | 3.550667 | 22.305110 | 2.173711 | 3.018231 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 2231.503197 | 134.737310 | 190.919882 | 62.943153 | 89.521803 | 439.824918 | 1050.477833 | 1118.496177 | 2231.503197 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 1.020439 | -0.920870 | -0.689841 | -0.463877 | -0.988082 | -0.252215 | -1.442236 | 1.020439 | 0.151278 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Variability | 0.861682 | -1.096534 | -0.392521 | 0.302748 | -1.083761 | 0.861682 | -0.554057 | 0.073985 | 0.219525 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Variability | 2.068204 | -0.058319 | 0.037827 | 0.179399 | -0.518248 | 2.068204 | 1.879935 | 1.257953 | 1.449844 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 1.598992 | -1.965279 | -1.508570 | -1.159467 | -0.504167 | -1.083442 | -0.162307 | 0.393753 | 1.598992 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Variability | 2.529960 | 1.451097 | 1.959743 | -0.594198 | 0.542825 | 2.180570 | 2.529960 | 1.262668 | 1.142364 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 1.954648 | -1.775951 | -1.570831 | -1.145823 | -0.496914 | -0.633560 | 0.374031 | 0.668831 | 1.954648 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 3.860297 | -1.744314 | -1.591805 | -0.613822 | -1.013155 | -0.444845 | -0.998354 | 3.860297 | 1.132919 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 1.254847 | -1.353628 | -0.955536 | -0.580691 | -0.936985 | 0.393878 | -0.724465 | 1.254847 | 0.456196 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Variability | 19.994449 | -0.530206 | -0.629968 | -0.736417 | 0.425404 | -0.499359 | 19.994449 | 0.719159 | 2.416603 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | ee Temporal Discontinuties | 3.102420 | -1.311671 | -1.165266 | -0.732171 | -0.845106 | -0.284489 | -1.133669 | 1.610292 | 3.102420 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Variability | 6.745734 | -0.981195 | -1.058802 | -0.340072 | -0.861187 | 6.745734 | 6.735090 | 4.653924 | 5.618963 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | ee Temporal Discontinuties | 19.343149 | -1.737120 | -1.204536 | -0.753270 | -0.289831 | 5.374617 | 5.647878 | 19.343149 | 10.423201 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 0.720728 | -1.413271 | -0.751086 | -0.630496 | -0.547635 | -0.690112 | -0.415645 | 0.720728 | -0.071578 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | ee Temporal Discontinuties | 1.438153 | -0.990601 | -1.283345 | -0.725418 | -0.753682 | 0.684958 | 0.749105 | 1.438153 | 0.788568 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | ee Temporal Discontinuties | 0.454017 | -0.615495 | -0.149270 | -1.003947 | -0.798047 | 0.340616 | -0.006517 | 0.363421 | 0.454017 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 1.643809 | -0.556028 | -1.397079 | -0.849979 | -0.878833 | -1.255444 | -0.324803 | 0.297748 | 1.643809 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | ee Temporal Discontinuties | 0.894536 | -0.495705 | -0.965821 | -0.995395 | -0.893566 | -0.771967 | -0.752331 | 0.894536 | 0.787647 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | ee Temporal Discontinuties | 0.497902 | -1.188442 | -1.157818 | -0.092556 | -1.290107 | -0.460462 | -1.204611 | 0.342429 | 0.497902 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 1.259115 | -1.268657 | -1.296362 | -0.442444 | -1.209108 | -0.359713 | -1.350061 | 1.259115 | -0.093713 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | N14 | digital_ok | nn Temporal Discontinuties | 2.327424 | -1.841286 | -1.076654 | -1.082735 | -1.019640 | -1.072860 | -1.885984 | 2.327424 | 0.075846 |